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Data-Engineer-Associate関連日本語版問題集 & Data-Engineer-Associate試験感想

何千人ものお客様がData-Engineer-Associate試験に合格し、関連する認定を取得しています。その後、Data-Engineer-Associate試験トレントはすべて、当社のWebサイトで購入されました。業界の動向に加えて、Data-Engineer-Associateテストガイドは、過去の多くの資料の厳密な分析によって書かれています。 Data-Engineer-Associate学習教材の言語は理解しやすいものであり、厳密な学習を行った場合のみ、最新の専門的なData-Engineer-Associate学習教材を作成します。私たちはあなたに最高のサービスを提供し、あなたが満足できることを願っています。

Data-Engineer-Associate試験資料の一つの利点は時間を節約できることです。言い換えば、受験者は短い時間をかけて勉強したら、Data-Engineer-Associate試験に合格できます。従って、Data-Engineer-Associate試験資料を勉強する時間が短くてもいいです。Data-Engineer-Associate試験資料はそんなにいい商品、何故選びませんか?また、弊社はいいサービスを提供します。Data-Engineer-Associate資料を勉強するとき、何か質問がありましたら、弊社と連絡できます。

>> Data-Engineer-Associate関連日本語版問題集 <<

真実的なData-Engineer-Associate関連日本語版問題集 & 合格スムーズData-Engineer-Associate試験感想 | 実用的なData-Engineer-Associate試験合格攻略 AWS Certified Data Engineer - Associate (DEA-C01)

GoShikenテストトレントを学習し、試験の準備をするのに20〜30時間しかかかりません。 Data-Engineer-Associate試験問題を購入した後、Data-Engineer-Associate試験トレントを学習し、主に仕事、家庭生活、学習に専念するために数時間を費やすだけです。 Data-Engineer-Associate試験問題の回答と質問は入念に選択され、試験の焦点をつかむため、試験の学習と準備に多くの時間を節約できます。合格率は98％以上と高いため、Data-Engineer-Associateガイドトレントを購入することで安心できます。

Amazon AWS Certified Data Engineer - Associate (DEA-C01) 認定 Data-Engineer-Associate 試験問題 (Q40-Q45):

質問 # 40
A company is planning to upgrade its Amazon Elastic Block Store (Amazon EBS) General Purpose SSD storage from gp2 to gp3. The company wants to prevent any interruptions in its Amazon EC2 instances that will cause data loss during the migration to the upgraded storage.
Which solution will meet these requirements with the LEAST operational overhead?

A. Create snapshots of the gp2 volumes. Create new gp3 volumes from the snapshots. Attach the new gp3 volumes to the EC2 instances.
B. Use AWS DataSync to create new gp3 volumes. Transfer the data from the original gp2 volumes to the new gp3 volumes.
C. Create new gp3 volumes. Gradually transfer the data to the new gp3 volumes. When the transfer is complete, mount the new gp3 volumes to the EC2 instances to replace the gp2 volumes.
D. Change the volume type of the existing gp2 volumes to gp3. Enter new values for volume size, IOPS, and throughput.

正解：D

解説：
Changing the volume type of the existing gp2 volumes to gp3 is the easiest and fastest way to migrate to the new storage type without any downtime or data loss. You can use the AWS Management Console, the AWS CLI, or the Amazon EC2 API to modify the volume type, size, IOPS, and throughput of your gp2 volumes.
The modification takes effect immediately, and you can monitor the progress of the modification using CloudWatch. The other options are either more complex or require additional steps, such as creating snapshots, transferring data, or attaching new volumes, which can increase the operational overhead and the risk of errors. References:
* Migrating Amazon EBS volumes from gp2 to gp3 and save up to 20% on costs (Section: How to migrate from gp2 to gp3)
* Switching from gp2 Volumes to gp3 Volumes to Lower AWS EBS Costs (Section: How to Switch from GP2 Volumes to GP3 Volumes)
* Modifying the volume type, IOPS, or size of an EBS volume - Amazon Elastic Compute Cloud (Section: Modifying the volume type)

質問 # 41
A company has a frontend ReactJS website that uses Amazon API Gateway to invoke REST APIs. The APIs perform the functionality of the website. A data engineer needs to write a Python script that can be occasionally invoked through API Gateway. The code must return results to API Gateway.
Which solution will meet these requirements with the LEAST operational overhead?

A. Deploy a custom Python script that can integrate with API Gateway on Amazon Elastic Kubernetes Service (Amazon EKS).
B. Create an AWS Lambda function. Ensure that the function is warm byscheduling an Amazon EventBridge rule to invoke the Lambda function every 5 minutes by usingmock events.
C. Create an AWS Lambda Python function with provisioned concurrency.
D. Deploy a custom Python script on an Amazon Elastic Container Service (Amazon ECS) cluster.

正解：C

解説：
AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers.
You can use Lambda to create functions that perform custom logic and integrate with other AWS services, such as API Gateway. Lambda automatically scales your application by running code in response to each trigger. You pay only for the compute time you consume1.
Amazon ECS is a fully managed container orchestration service that allows you to run and scale containerized applications on AWS. You can use ECS to deploy, manage, and scale Docker containers using either Amazon EC2 instances or AWS Fargate, a serverless compute engine for containers2.
Amazon EKS is a fully managed Kubernetes service that allows you to run Kubernetes clusters on AWS without needing to install, operate, or maintain your own Kubernetes control plane. You can use EKS to deploy, manage, and scale containerized applications using Kubernetes on AWS3.
The solution that meets the requirements with the least operational overhead is to create an AWS Lambda Python function with provisioned concurrency. This solution has the following advantages:
It does not require you to provision, manage, or scale any servers or clusters, as Lambda handles all the infrastructure for you. This reduces the operational complexity and cost of running your code.
It allows you to write your Python script as a Lambda function and integrate it with API Gateway using a simple configuration. API Gateway can invoke your Lambda function synchronously or asynchronously, and return the results to the frontend website.
It ensures that your Lambda function is ready to respond to API requests without any cold start delays, by using provisioned concurrency. Provisioned concurrency is a feature that keeps your function initialized and hyper-ready to respond in double-digit milliseconds. You can specify the number of concurrent executions that you want to provision for your function.
Option A is incorrect because it requires you to deploy a custom Python script on an Amazon ECS cluster.
This solution has the following disadvantages:
It requires you to provision, manage, and scale your own ECS cluster, either using EC2 instances or Fargate. This increases the operational complexity and cost of running your code.
It requires you to package your Python script as a Docker container image and store it in a container registry, such as Amazon ECR or Docker Hub. This adds an extra step to your deployment process.
It requires you to configure your ECS cluster to integrate with API Gateway, either using an Application Load Balancer or a Network Load Balancer. This adds another layer of complexity to your architecture.
Option C is incorrect because it requires you to deploy a custom Python script that can integrate with API Gateway on Amazon EKS. This solution has the following disadvantages:
It requires you to provision, manage, and scale your own EKS cluster, either using EC2 instances or Fargate. This increases the operational complexity and cost of running your code.
It requires you to package your Python script as a Docker container image and store it in a container registry, such as Amazon ECR or Docker Hub. This adds an extra step to your deployment process.
It requires you to configure your EKS cluster to integrate with API Gateway, either using an Application Load Balancer, a Network Load Balancer, or a service of type LoadBalancer. This adds another layer of complexity to your architecture.
Option D is incorrect because it requires you to create an AWS Lambda function and ensure that the function is warm by scheduling an Amazon EventBridge rule to invoke the Lambda function every 5 minutes by using mock events. This solution has the following disadvantages:
It does not guarantee that your Lambda function will always be warm, as Lambda may scale down your function if it does not receive any requests for a long period of time. This may cause cold start delays when your function is invoked by API Gateway.
It incurs unnecessary costs, as you pay for the compute time of your Lambda function every time it is invoked by the EventBridge rule, even if it does not perform any useful work1.
References:
1: AWS Lambda - Features
2: Amazon Elastic Container Service - Features
3: Amazon Elastic Kubernetes Service - Features
[4]: Building API Gateway REST API with Lambda integration - Amazon API Gateway
[5]: Improving latency with Provisioned Concurrency - AWS Lambda
[6]: Integrating Amazon ECS with Amazon API Gateway - Amazon Elastic Container Service
[7]: Integrating Amazon EKS with Amazon API Gateway - Amazon Elastic Kubernetes Service
[8]: Managing concurrency for a Lambda function - AWS Lambda

質問 # 42
During a security review, a company identified a vulnerability in an AWS Glue job. The company discovered that credentials to access an Amazon Redshift cluster were hard coded in the job script.
A data engineer must remediate the security vulnerability in the AWS Glue job. The solution must securely store the credentials.
Which combination of steps should the data engineer take to meet these requirements? (Choose two.)

A. Store the credentials in the AWS Glue job parameters.
B. Access the credentials from a configuration file that is in an Amazon S3 bucket by using the AWS Glue job.
C. Store the credentials in a configuration file that is in an Amazon S3 bucket.
D. Grant the AWS Glue job 1AM role access to the stored credentials.
E. Store the credentials in AWS Secrets Manager.

正解：D、E

解説：
AWS Secrets Manager is a service that allows you to securely store and manage secrets, such as database credentials, API keys, passwords, etc. You can use Secrets Manager to encrypt, rotate, and audit your secrets, as well as to control access to them using fine-grained policies. AWS Glue is a fully managed service that provides a serverless data integration platform for data preparation, data cataloging, and data loading. AWS Glue jobs allow you to transform and load data from various sources into various targets, using either a graphical interface (AWS Glue Studio) or a code-based interface (AWS Glue console or AWS Glue API).
Storing the credentials in AWS Secrets Manager and granting the AWS Glue job 1AM role access to the stored credentials will meet the requirements, as it will remediate the security vulnerability in the AWS Glue job and securely store the credentials. By using AWS Secrets Manager, you can avoid hard coding the credentials in the job script, which is a bad practice that exposes the credentials to unauthorized access or leakage. Instead, you can store the credentials as a secret in Secrets Manager and reference the secret name or ARN in the job script. You can also use Secrets Manager to encrypt thecredentials using AWS Key Management Service (AWS KMS), rotate the credentials automatically or on demand, and monitor the access to the credentials using AWS CloudTrail. By granting the AWS Glue job 1AM role access to the stored credentials, you can use the principle of least privilege to ensure that only the AWS Glue job can retrieve the credentials from Secrets Manager. You can also use resource-based or tag-based policies to further restrict the access to the credentials.
The other options are not as secure as storing the credentials in AWS Secrets Manager and granting the AWS Glue job 1AM role access to the stored credentials. Storing the credentials in the AWS Glue job parameters will not remediate the security vulnerability, as the job parameters are still visible in the AWS Glue console and API. Storing the credentials in a configuration file that is in an Amazon S3 bucket and accessing the credentials from the configuration file by using the AWS Glue job will not be as secure as using Secrets Manager, as the configuration file may not be encrypted or rotated, and the access to the file may not be audited or controlled. References:
AWS Secrets Manager
AWS Glue
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 6: Data Integration and Transformation, Section 6.1: AWS Glue

質問 # 43
A company needs to build a data lake in AWS. The company must provide row-level data access and column- level data access to specific teams. The teams will access the data by using Amazon Athena, Amazon Redshift Spectrum, and Apache Hive from Amazon EMR.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon S3 for data lake storage. Use S3 access policies to restrict data access by rows and columns. Provide data access through Amazon S3.
B. Use Amazon S3 for data lake storage. Use Apache Ranger through Amazon EMR to restrict data access by rows and columns. Provide data access by using Apache Pig.
C. Use Amazon Redshift for data lake storage. Use Redshift security policies to restrict data access by rows and columns. Provide data access by using Apache Spark and Amazon Athena federated queries.
D. Use Amazon S3 for data lake storage. Use AWS Lake Formation to restrict data access by rows and columns. Provide data access through AWS Lake Formation.

正解：D

解説：
Option D is the best solution to meet the requirements with the least operational overhead because AWS Lake Formation is a fully managed service that simplifies the process of building, securing, and managing data lakes. AWS Lake Formation allows you to define granular data access policies at the row and column level for different users and groups. AWS Lake Formation also integrates with Amazon Athena, Amazon RedshiftSpectrum, and Apache Hive on Amazon EMR, enabling these services to access the data in the data lake through AWS Lake Formation.
Option A is not a good solution because S3 access policies cannot restrict data access by rows and columns.
S3 access policies are based on the identity and permissions of the requester, the bucket and object ownership, and the object prefix and tags. S3 access policies cannot enforce fine-grained data access control at the row and column level.
Option B is not a good solution because it involves using Apache Ranger and Apache Pig, which are not fully managed services and require additional configuration and maintenance. Apache Ranger is a framework that provides centralized security administration for data stored in Hadoop clusters, such as Amazon EMR.
Apache Ranger can enforce row-level and column-level access policies for Apache Hive tables. However, Apache Ranger is not a native AWS service and requires manual installation and configuration on Amazon EMR clusters. Apache Pig is a platform that allows you to analyze large data sets using a high-level scripting language called Pig Latin. Apache Pig can access data stored in Amazon S3 and process it using Apache Hive. However, Apache Pig is not a native AWS service and requires manual installation and configuration on Amazon EMR clusters.
Option C is not a good solution because Amazon Redshift is not a suitable service for data lake storage.
Amazon Redshift is a fully managed data warehouse service that allows you to run complex analytical queries using standard SQL. Amazon Redshift can enforce row-level and column-level access policies for different users and groups. However, Amazon Redshift is not designed to store and process large volumes of unstructured or semi-structured data, which are typical characteristics of data lakes. Amazon Redshift is also more expensive and less scalable than Amazon S3 for data lake storage.
:
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
What Is AWS Lake Formation? - AWS Lake Formation
Using AWS Lake Formation with Amazon Athena - AWS Lake Formation
Using AWS Lake Formation with Amazon Redshift Spectrum - AWS Lake Formation Using AWS Lake Formation with Apache Hive on Amazon EMR - AWS Lake Formation Using Bucket Policies and User Policies - Amazon Simple Storage Service Apache Ranger Apache Pig What Is Amazon Redshift? - Amazon Redshift

質問 # 44
A company receives a data file from a partner each day in an Amazon S3 bucket. The company uses a daily AW5 Glue extract, transform, and load (ETL) pipeline to clean and transform each data file. The output of the ETL pipeline is written to a CSV file named Dairy.csv in a second 53 bucket.
Occasionally, the daily data file is empty or is missing values for required fields. When the file is missing data, the company can use the previous day's CSV file.
A data engineer needs to ensure that the previous day's data file is overwritten only if the new daily file is complete and valid.
Which solution will meet these requirements with the LEAST effort?

A. Run a SQL query in Amazon Athena to read the CSV file and drop missing rows. Copy the corrected CSV file to the second S3 bucket.
B. Invoke an AWS Lambda function to check the file for missing data and to fill in missing values in required fields.
C. Use AWS Glue Studio to change the code in the ETL pipeline to fill in any missing values in the required fields with the most common values for each field.
D. Configure the AWS Glue ETL pipeline to use AWS Glue Data Quality rules. Develop rules in Data Quality Definition Language (DQDL) to check for missing values in required files and empty files.

正解：D

解説：
Problem Analysis:
The company runs a daily AWS Glue ETL pipeline to clean and transform files received in an S3 bucket.
If a file is incomplete or empty, the previous day's file should be retained.
Need a solution to validate files before overwriting the existing file.
Key Considerations:
Automate data validation with minimal human intervention.
Use built-in AWS Glue capabilities for ease of integration.
Ensure robust validation for missing or incomplete data.
Solution Analysis:
Option A: Lambda Function for Validation
Lambda can validate files, but it would require custom code.
Does not leverage AWS Glue's built-in features, adding operational complexity.
Option B: AWS Glue Data Quality Rules
AWS Glue Data Quality allows defining Data Quality Definition Language (DQDL) rules.
Rules can validate if required fields are missing or if the file is empty.
Automatically integrates into the existing ETL pipeline.
If validation fails, retain the previous day's file.
Option C: AWS Glue Studio with Filling Missing Values
Modifying ETL code to fill missing values with most common values risks introducing inaccuracies.
Does not handle empty files effectively.
Option D: Athena Query for Validation
Athena can drop rows with missing values, but this is a post-hoc solution.
Requires manual intervention to copy the corrected file to S3, increasing complexity.
Final Recommendation:
Use AWS Glue Data Quality to define validation rules in DQDL for identifying missing or incomplete data.
This solution integrates seamlessly with the ETL pipeline and minimizes manual effort.
Implementation Steps:
Enable AWS Glue Data Quality in the existing ETL pipeline.
Define DQDL Rules, such as:
Check if a file is empty.
Verify required fields are present and non-null.
Configure the pipeline to proceed with overwriting only if the file passes validation.
In case of failure, retain the previous day's file.
Reference:
AWS Glue Data Quality Overview
Defining DQDL Rules
AWS Glue Studio Documentation

質問 # 45
......

話と行動の距離はどのぐらいありますか。これは人の心によることです。意志が強い人にとって、行動は目と鼻の先にあるのです。あなたはきっとこのような人でしょう。AmazonのData-Engineer-Associate認定試験に申し込んだ以上、試験に合格しなければならないです。これもあなたの意志が強いことを表示する方法です。GoShikenが提供したトレーニング資料はインターネットで最高のものです。AmazonのData-Engineer-Associate認定試験に合格したいのなら、GoShikenのAmazonのData-Engineer-Associate試験トレーニング資料を利用してください。

Data-Engineer-Associate試験感想: https://www.goshiken.com/Amazon/Data-Engineer-Associate-mondaishu.html

時間が経つとともに、GoShiken Data-Engineer-Associate試験感想はより多くの受験生から大好評を博します、Amazon Data-Engineer-Associate関連日本語版問題集とにかく、これは皆さんのキャリアに大きな影響をもたらせる試験です、インタネット時代に当たるなので、パソコン上のAmazonのData-Engineer-Associate試験についての情報は複雑で区別するのは困難なことであると思われます、Amazon Data-Engineer-Associate関連日本語版問題集領収書について：社名入りの領収書が必要な場合、メールで社名に記入していただき送信してください、これにより、Data-Engineer-Associateテストガイドを使用したい限り、学習状態に入ることができます、Amazon Data-Engineer-Associate関連日本語版問題集我々は、革新の仕組みを近代化し、強力な専門家を育成することによって、研究開発能力を高めようとしています。

そもそも休止する必要も無かったんじゃないのか、ジークヴァルト様だって、領地の経営と騎士Data-Engineer-Associateのお仕事を両立されています、時間が経つとともに、GoShikenはより多くの受験生から大好評を博します、とにかく、これは皆さんのキャリアに大きな影響をもたらせる試験です。

素敵なData-Engineer-Associate関連日本語版問題集試験-試験の準備方法-効率的なData-Engineer-Associate試験感想

インタネット時代に当たるなので、パソコン上のAmazonのData-Engineer-Associate試験についての情報は複雑で区別するのは困難なことであると思われます、領収書について：社名入りの領収書が必要な場合、メールで社名に記入していただき送信してください。

これにより、Data-Engineer-Associateテストガイドを使用したい限り、学習状態に入ることができます。